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c0webster

Hardened Google Workspace MCP

by c0webster

create_table_with_data

Create and populate tables in Google Docs using structured data. Follow a mandatory workflow: first inspect document structure to get the correct insertion index, then format data as a 2D list of strings.

Instructions

Creates a table and populates it with data in one reliable operation.

CRITICAL: YOU MUST CALL inspect_doc_structure FIRST TO GET THE INDEX!

MANDATORY WORKFLOW - DO THESE STEPS IN ORDER:

Step 1: ALWAYS call inspect_doc_structure first Step 2: Use the 'total_length' value from inspect_doc_structure as your index Step 3: Format data as 2D list: [["col1", "col2"], ["row1col1", "row1col2"]] Step 4: Call this function with the correct index and data

EXAMPLE DATA FORMAT: table_data = [ ["Header1", "Header2", "Header3"], # Row 0 - headers ["Data1", "Data2", "Data3"], # Row 1 - first data row ["Data4", "Data5", "Data6"] # Row 2 - second data row ]

CRITICAL INDEX REQUIREMENTS:

  • NEVER use index values like 1, 2, 10 without calling inspect_doc_structure first

  • ALWAYS get index from inspect_doc_structure 'total_length' field

  • Index must be a valid insertion point in the document

DATA FORMAT REQUIREMENTS:

  • Must be 2D list of strings only

  • Each inner list = one table row

  • All rows MUST have same number of columns

  • Use empty strings "" for empty cells, never None

  • Use debug_table_structure after creation to verify results

Args: user_google_email: User's Google email address document_id: ID of the document to update table_data: 2D list of strings - EXACT format: [["col1", "col2"], ["row1col1", "row1col2"]] index: Document position (MANDATORY: get from inspect_doc_structure 'total_length') bold_headers: Whether to make first row bold (default: true)

Returns: str: Confirmation with table details and link

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
user_google_emailYes
document_idYes
table_dataYes
indexYes
bold_headersNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It thoroughly explains critical behavioral traits: the mandatory workflow with 'inspect_doc_structure', data format requirements (2D list of strings, uniform columns, no None values), index validation needs, and post-creation verification with 'debug_table_structure'. It also mentions the tool's reliability aspect ('one reliable operation') and output details (confirmation with table details and link).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and well-structured with clear sections (purpose, critical notes, workflow, examples, requirements, args, returns). While comprehensive, some redundancy exists (e.g., repeating 'CRITICAL' points), and the workflow steps could be more condensed. However, every sentence adds value, and it is front-loaded with the core purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex mutation tool with 5 parameters, 0% schema coverage, no annotations, and an output schema, the description is exceptionally complete. It covers the purpose, prerequisites, step-by-step usage, parameter semantics, data formatting rules, index requirements, post-creation verification, and return value explanation. The output schema exists, so the description appropriately focuses on usage rather than return structure.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Given 0% schema description coverage, the description fully compensates by providing detailed semantics for all parameters. It explains 'user_google_email' and 'document_id' in the context of updating a document, specifies the exact format and constraints for 'table_data' with examples, details how to obtain and validate 'index' from 'inspect_doc_structure', and clarifies the effect of 'bold_headers'. This adds significant meaning beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool 'Creates a table and populates it with data in one reliable operation,' which is a specific verb+resource+outcome combination. It clearly distinguishes this from sibling tools like 'create_doc' or 'insert_doc_elements' by focusing on table creation with data insertion, not general document creation or element insertion.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit, step-by-step instructions on when and how to use this tool, including mandatory prerequisites ('ALWAYS call inspect_doc_structure first'), alternatives (using 'debug_table_structure after creation to verify results'), and a clear workflow. It explicitly states critical requirements and exclusions, such as never using arbitrary index values without prior inspection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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